SoroushVahidi/scidocs huggingface dataset:
A Formal Analysis of the ISO 9241-210 Definition of User Experience
(2015)

User Experience (UX) is a major concept in HCI and a variety of different UX definitions have been suggested within the scientific community. An ISO UX definition has been presented to standardize the term from an industry perspective. We introduce methods from formal logic in order to formalize and analyze the ISO UX definition with regard to consistency and ambiguities and present recommendations for an improved version. Although this kind of formalization is not common within the CHI community, we show that quasi-formal methods provide an alternative way of analyzing widely discussed HCI terms, such as UX, to deepen its understanding.
THE IMPACT OF CHANGE MANAGEMENT IN ERP SYSTEM : A CASE STUDY OF MADAR
(2011)

This paper discusses Enterprise Resource Planning (ERP) change management. A review of the research literature has been presented that focuses on the ERP change management factors. The literature is further classified and the major outcomes of each study have been addressed in this paper. The discussion is supported by a practical ERP project called Madar. The paper is targeted to investigate and identify the reasons for resistance to diffusion and why individuals within an organization resist the changes. This paper also suggests strategies to minimize the resistance if not overcome completely.
Walking to public transit: steps to help meet physical activity recommendations.
(2005)

BACKGROUND
Nearly half of Americans do not meet the Surgeon General's recommendation of > or =30 minutes of physical activity daily. Some transit users may achieve 30 minutes of physical activity daily solely by walking to and from transit. This study estimates the total daily time spent walking to and from transit and the predictors of achieving 30 minutes of physical activity daily by doing so.


METHODS
Transit-associated walking times for 3312 transit users were examined among the 105,942 adult respondents to the 2001 National Household Travel Survey, a telephone-based survey sponsored by the U.S. Department of Transportation to assess American travel behavior.


RESULTS
Americans who use transit spend a median of 19 minutes daily walking to and from transit; 29% achieve > or =30 minutes of physical activity a day solely by walking to and from transit. In multivariate analysis, rail users, minorities, people in households earning <$15,000 a year, and people in high-density urban areas were more likely to spend > or =30 minutes walking to and from transit daily.


CONCLUSIONS
Walking to and from public transportation can help physically inactive populations, especially low-income and minority groups, attain the recommended level of daily physical activity. Increased access to public transit may help promote and maintain active lifestyles. Results from this study may contribute to health impact assessment studies (HIA) that evaluate the impact of proposed public transit systems on physical activity levels, and thereby may influence choices made by transportation planners.
Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation
(2014)

With the explosive growth of web data, effective and efficient technologies are in urgent need for retrieving semantically relevant contents of heterogeneous modalities. Previous studies devote efforts to modeling simple cross-modal statistical dependencies, and globally projecting the heterogeneous modalities into a measurable subspace. However, global projections cannot appropriately adapt to diverse contents, and the naturally existing multilevel semantic relation in web data is ignored. We study the problem of semantic coherent retrieval, where documents from different modalities should be ranked by the semantic relevance to the query. Accordingly, we propose TINA, a correlation learning method by adaptive hierarchical semantic aggregation. First, by joint modeling of content and ontology similarities, we build a semantic hierarchy to measure multilevel semantic relevance. Second, with a set of local linear projections and probabilistic membership functions, we propose two paradigms for local expert aggregation, i.e., local projection aggregation and local distance aggregation. To learn the cross-modal projections, we optimize the structure risk objective function that involves semantic coherence measurement, local projection consistency, and the complexity penalty of local projections. Compared to existing approaches, a better bias-variance tradeoff is achieved by TINA in real-world cross-modal correlation learning tasks. Extensive experiments on widely used NUS-WIDE and ICML-Challenge for image-text retrieval demonstrate that TINA better adapts to the multilevel semantic relation and content divergence, and, thus, outperforms state of the art with better semantic coherence.
IT Control in the Australian Public Sector: An International Comparison
(2005)

Despite widespread adoption of IT control frameworks, little academic empirical research has been undertaken to investigate their use. This paper reports upon research to benchmark the maturity levels of 15 key IT control processes from the Control Objectives for Information and Related Technology (COBIT) in public sector organisations across Australia. It also makes a comparison against a similar benchmark for a mixed sector group from a range of nations, a mixed sector group from Asian-Oceanic nations, and for public sector organisations for all geographic areas. The Australian data were collected in a mail survey of the 387 non-financial public sector organisations identified as having more than 50 employees, which returned a 27% response rate. Patterns seen in the original international survey undertaken by the IS Audit and Control Association in 2002 were also seen in the Australian data. However, the Australian public sector performed better than sectors in all the international benchmarks for the 15 most important IT processes.
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
(2017)

We study the resilience to Byzantine failures of distributed implementations of Stochastic Gradient Descent (SGD). So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones. Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system. Assuming a set of n workers, up to f being Byzantine, we ask how resilient can SGD be, without limiting the dimension, nor the size of the parameter space. We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers (i.e, current approaches) tolerates a single Byzantine failure. We then formulate a resilience property of the aggregation rule capturing the basic requirements to guarantee convergence despite f Byzantine workers. We propose Krum, an aggregation rule that satisfies our resilience property, which we argue is the first provably Byzantine-resilient algorithm for distributed SGD. We also report on experimental evaluations of Krum.
Deep learning with Elastic Averaging SGD
(2015)

We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master). The algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master. We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance. We propose synchronous and asynchronous variants of the new algorithm. We provide the stability analysis of the asynchronous variant in the round-robin scheme and compare it with the more common parallelized method ADMM. We show that the stability of EASGD is guaranteed when a simple stability condition is satisfied, which is not the case for ADMM. We additionally propose the momentum-based version of our algorithm that can be applied in both synchronous and asynchronous settings. Asynchronous variant of the algorithm is applied to train convolutional neural networks for image classification on the CIFAR and ImageNet datasets. Experiments demonstrate that the new algorithm accelerates the training of deep architectures compared to DOWNPOUR and other common baseline approaches and furthermore is very communication efficient.
Large-scale matrix factorization with distributed stochastic gradient descent
(2011)

We provide a novel algorithm to approximately factor large matrices with millions of rows, millions of columns, and billions of nonzero elements. Our approach rests on stochastic gradient descent (SGD), an iterative stochastic optimization algorithm. We first develop a novel "stratified" SGD variant (SSGD) that applies to general loss-minimization problems in which the loss function can be expressed as a weighted sum of "stratum losses." We establish sufficient conditions for convergence of SSGD using results from stochastic approximation theory and regenerative process theory. We then specialize SSGD to obtain a new matrix-factorization algorithm, called DSGD, that can be fully distributed and run on web-scale datasets using, e.g., MapReduce. DSGD can handle a wide variety of matrix factorizations. We describe the practical techniques used to optimize performance in our DSGD implementation. Experiments suggest that DSGD converges significantly faster and has better scalability properties than alternative algorithms.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
(2014)

We present an integrated framework for using Convolutional Networks for classification , localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat.
ON MILLIMETER-WAVE IMAGING OF CONCEALED OBJECTS : APPLICATION USING BACK-PROJECTION ALGORITHM
(2012)

Millimeter-wave (MMW) imaging is a powerful tool for the detection of objects concealed under clothing. Several factors including different kinds of objects, variety of covering materials and their thickness, accurate imaging of near-field scattered data affect the success of detection. To practice with such considerations, this paper presents the two-dimensional (2D) images of different targets hidden under various fabric sheets. The W-band inverse synthetic aperture radar (ISAR) data of various target-covering situations are acquired and imaged by applying both the focusing operator based inversion algorithm and the spherical back-projection algorithm. Results of these algorithms are demonstrated and compared to each other to assess the performance of the MMW imaging in detecting the concealed objects of both metallic and dielectric types.
A study of prefix hijacking and interception in the internet
(2007)

There have been many incidents of prefix hijacking in the Internet. The hijacking AS can blackhole the hijacked traffic. Alternatively, it can transparently intercept the hijacked traffic by forwarding it onto the owner. This paper presents a study of such prefix hijacking and interception with the following contributions: (1). We present a methodology for prefix interception, (2). We estimate the fraction of traffic to any prefix that can be hijacked and intercepted in the Internet today, (3). The interception methodology is implemented and used to intercept real traffic to our prefix, (4). We conduct a detailed study to detect ongoing prefix interception.
 We find that: Our hijacking estimates are in line with the impact of past hijacking incidents and show that ASes higher up in the routing hierarchy can hijack a significant amount of traffic to any prefix, including popular prefixes. A less apparent result is that the same holds for prefix interception too. Further, our implementation shows that intercepting traffic to a prefix in the Internet is almost as simple as hijacking it. Finally, while we fail to detect ongoing prefix interception, the detection exercise highlights some of the challenges posed by the prefix interception problem.
Development of a Dynamic Simulation Tool for the ExoMars Rover
(2005)

Future planetary missions, including the 2011 European Space Agency (ESA) ExoMars mission, will require rovers to travel further, faster, and over more demanding terrain than has been encountered to date. To improve overall mobility, advances need to be made in autonomous navigation, power collection, and locomotion. In this paper we focus on the locomotion problem and discuss the development of a planetary rover chassis simulation tool that allows us to study key locomotion test cases such as slope climbing in loose soil. We have also constructed rover wheels and obtained experimental data to validate the wheel-soil interaction module. The main conclusion is that to fully validate such a complex simulation, experimental data from a full rover chassis is required. This is a first step in an on-going effort to validate the simulation with experimental data obtained from a full rover prototype.
Accurate Angle Estimator for High-Frame-Rate 2-D Vector Flow Imaging
(2016)

This paper presents a novel approach for estimating 2-D flow angles using a high-frame-rate ultrasound method. The angle estimator features high accuracy and low standard deviation (SD) over the full 360° range. The method is validated on Field II simulations and phantom measurements using the experimental ultrasound scanner SARUS and a flow rig before being tested in vivo. An 8-MHz linear array transducer is used with defocused beam emissions. In the simulations of a spinning disk phantom, a 360° uniform behavior on the angle estimation is observed with a median angle bias of 1.01° and a median angle SD of 1.8°. Similar results are obtained on a straight vessel for both simulations and measurements, where the obtained angle biases are below 1.5° with SDs around 1°. Estimated velocity magnitudes are also kept under 10% bias and 5% relative SD in both simulations and measurements. An in vivo measurement is performed on a carotid bifurcation of a healthy individual. A 3-s acquisition during three heart cycles is captured. A consistent and repetitive vortex is observed in the carotid bulb during systoles.
Predictive Data Mining for Diagnosis of Thyroid Disease using Neural Network
(2015)

This paper presents a systematic approach for earlier diagnosis of Thyroid disease using back propagation algorithm used in neural network. Back propagation algorithm is a widely used algorithm in this field. ANN has been developed based on back propagation of error used for earlier prediction of disease. ANN was subsequently trained with experimental data and testing is carried out using data that was not used during training process. Results show that outcome of ANN is in good agreement with experimental data; this indicates that developed neural network can be used as an alternative for earlier prediction of a disease. Keywords—Back propagation, decision tree, gradient descent, prediction, Supervised Learning
A Robust Single-Phase PLL System With Stable and Fast Tracking
(2008)

Phase, frequency, and amplitude of single-phase voltages are the most important and basic information required for single-phase grid-connected applications. This paper proposes a method for instantly and robustly estimating the phase, frequency, and amplitude of frequency-varying single-phase signals for such applications, which is a phase-locked loop (PLL) method based on a structure. The proposed method has the following attractive features: 1) the estimation system results in a nonlinear system, but it can be stabilized; 2) all subsystems constructing the system can be easily designed; 3) "two-phase signal generator"; can autotune a single system parameter in response to varying frequency of the injected signal; 4) high-order "PLL-Controllers" allowing fast tracking can be stably used; and 5) even in hostile envelopments, favorable instant estimates can be obtained. This paper proposes the estimation method and verifies its usefulness by extensive numerical experiments.
MixedFusion: Real-Time Reconstruction of an Indoor Scene with Dynamic Objects
(2018)

Real-time indoor scene reconstruction aims to recover the 3D geometry of an indoor scene in real time with a sensor scanning the scene. Previous works of this topic consider pure static scenes, but in this paper, we focus on more challenging cases that the scene contains dynamic objects, for example, moving people and floating curtains, which are quite common in reality and thus are eagerly required to be handled. We develop an end-to-end system using a depth sensor to scan a scene on the fly. By proposing a Sigmoid-based Iterative Closest Point (S-ICP) method, we decouple the camera motion and the scene motion from the input sequence and segment the scene into static and dynamic parts accordingly. The static part is used to estimate the camera rigid motion, while for the dynamic part, graph node-based motion representation and model-to-depth fitting are applied to reconstruct the scene motions. With the camera and scene motions reconstructed, we further propose a novel mixed voxel allocation scheme to handle static and dynamic scene parts with different mechanisms, which helps to gradually fuse a large scene with both static and dynamic objects. Experiments show that our technique successfully fuses the geometry of both the static and dynamic objects in a scene in real time, which extends the usage of the current techniques for indoor scene reconstruction.
Faceted Search
(2009)

We live in an information age that requires us, more than ever, to represent, access, and use information. Over the last several decades, we have developed a modern science and technology for information retrieval, relentlessly pursuing the vision of a “memex” that Vannevar Bush proposed in his seminal article, “As We May Think.” Faceted search plays a key role in this program. Faceted search addresses weaknesses of conventional search approaches and has emerged as a foundation for interactive information retrieval. User studies demonstrate that faceted search provides more effective information-seeking support to users than best-first search. Indeed, faceted search has become increasingly prevalent in online information access systems, particularly for e-commerce and site search. In this lecture, we explore the history, theory, and practice of faceted search. Although we cannot hope to be exhaustive, our aim is to provide sufficient depth and breadth to offer a useful resource to both researchers and practitioners. Because faceted search is an area of interest to computer scientists, information scientists, interface designers, and usability researchers, we do not assume that the reader is a specialist in any of these fields. Rather, we offer a self-contained treatment of the topic, with an extensive bibliography for those who would like to pursue particular aspects in more depth.
Classifying distinct basal cell carcinoma subtype by means of dermatoscopy and reflectance confocal microscopy.
(2014)

BACKGROUND
The current guidelines for the management of basal cell carcinoma (BCC) suggest a different therapeutic approach according to histopathologic subtype. Although dermatoscopic and confocal criteria of BCC have been investigated, no specific studies were performed to evaluate the distinct reflectance confocal microscopy (RCM) aspects of BCC subtypes.


OBJECTIVES
To define the specific dermatoscopic and confocal criteria for delineating different BCC subtypes.


METHODS
Dermatoscopic and confocal images of histopathologically confirmed BCCs were retrospectively evaluated for the presence of predefined criteria. Frequencies of dermatoscopic and confocal parameters are provided. Univariate and adjusted odds ratios were calculated. Discriminant analyses were performed to define the independent confocal criteria for distinct BCC subtypes.


RESULTS
Eighty-eight BCCs were included. Dermatoscopically, superficial BCCs (n=44) were primarily typified by the presence of fine telangiectasia, multiple erosions, leaf-like structures, and revealed cords connected to the epidermis and epidermal streaming upon RCM. Nodular BCCs (n=22) featured the classic dermatoscopic features and well outlined large basaloid islands upon RCM. Infiltrative BCCs (n=22) featured structureless, shiny red areas, fine telangiectasia, and arborizing vessels on dermatoscopy and dark silhouettes upon RCM.


LIMITATIONS
The retrospective design.


CONCLUSION
Dermatoscopy and confocal microscopy can reliably classify different BCC subtypes.
Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPs
(2008)

In this paper we address reinforcement learning problems with continuous state-action spaces. We propose a new algorithm, tted natural actor-critic (FNAC), that extends the work in [1] to allow for general function approximation and data reuse. We combine the natural actor-critic architecture [1] with a variant of tted value iteration using importance sampling. The method thus obtained combines the appealing features of both approaches while overcoming their main weaknesses: the use of a gradient-based actor readily overcomes the di culties found in regression methods with policy optimization in continuous action-spaces; in turn, the use of a regression-based critic allows for e cient use of data and avoids convergence problems that TD-based critics often exhibit. We establish the convergence of our algorithm and illustrate its application in a simple continuous space, continuous action problem.
Learning Discriminative Affine Regions via
(2017)

We present an accurate method for estimation of the affine shape of local features. The method is trained in a novel way, exploiting the recently proposed HardNet triplet loss. The loss function is driven by patch descriptor differences, avoiding problems with symmetries. Moreover, such training process does not require precisely geometrically aligned patches. The affine shape is represented in a way amenable to learning by stochastic gradient descent. When plugged into a state-of-the-art wide baseline matching algorithm, the performance on standard datasets improves in both the number of challenging pairs matched and the number of inliers. Finally, AffNet with combination of Hessian detector and HardNet descriptor improves bag-of-visual-words based state of the art on Oxford5k and Paris6k by large margin, 4.5 and 4.2 mAP points respectively. The source code and trained networks are available at https://github.com/ducha-aiki/affnet